Hugging Face Community Evals

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware.

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Buy it · $12 Read it before you buy $12 Written by huggingface · unverified publisher
Context cost
7.5k tokensestimated from the bundle, loaded when it triggers
Bundle
6 files · 30.2 kB3 scripts among them — read before you run
Licence
Apache-2.0paid listing
Last change
no release on file
Servers it uses
Noneruns standalone

What it does

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.

Installed, it changes the agent in these ways.

What this skill changes about the agent is not written down here yet. The listing was collected from its source, and the description is in its own SKILL.md.

Workflow

Runs a procedure end to end.

evaluationbenchmarkslighteval

The skill itself

This is the whole product. A skill is instructions the model reads, so there is nothing behind the listing you cannot see first — the front matter loads with every session, and the body below it loads when the skill triggers.

SKILL.md6.6 kB · 208 lines
--- name: huggingface-community-evals description: Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation. ---
6# Overview
7
8This skill is for **running evaluations against models on the Hugging Face Hub on local hardware**.
9
10It covers:
11- inspect-ai with local inference
12- lighteval with local inference
13- choosing between vllm, Hugging Face Transformers, and accelerate
14- smoke tests, task selection, and backend fallback strategy
15
16It does **not** cover:
17- Hugging Face Jobs orchestration
18- model-card or model-index edits
19- README table extraction
20- Artificial Analysis imports
21- .eval_results generation or publishing
22- PR creation or community-evals automation
23
24If the user wants to **run the same eval remotely on Hugging Face Jobs**, hand off to the hugging-face-jobs skill and pass it one of the local scripts in this skill.
25
26If the user wants to **publish results into the community evals workflow**, stop after generating the evaluation run and hand off that publishing step to ~/code/community-evals.
27
28> All paths below are relative to the directory containing this SKILL.md.
29
30# When To Use Which Script
31
32| Use case | Script |
33|---|---|
34| Local inspect-ai eval on a Hub model via inference providers | scripts/inspect_eval_uv.py |
35| Local GPU eval with inspect-ai using vllm or Transformers | scripts/inspect_vllm_uv.py |
36| Local GPU eval with lighteval using vllm or accelerate | scripts/lighteval_vllm_uv.py |
37| Extra command patterns | examples/USAGE_EXAMPLES.md |
38
39# Prerequisites
40
41- Prefer uv run for local execution.
42- Set HF_TOKEN for gated/private models.
43- For local GPU runs, verify GPU access before starting:
44
45```bash
46uv --version
47printenv HF_TOKEN >/dev/null
48nvidia-smi
49```
50
51If nvidia-smi is unavailable, either:
52- use scripts/inspect_eval_uv.py for lighter provider-backed evaluation, or
53- hand off to the hugging-face-jobs skill if the user wants remote compute.
54
55# Core Workflow
56
571. Choose the evaluation framework.
58 - Use inspect-ai when you want explicit task control and inspect-native flows.
59 - Use lighteval when the benchmark is naturally expressed as a lighteval task string, especially leaderboard-style tasks.
602. Choose the inference backend.
61 - Prefer vllm for throughput on supported architectures.
62 - Use Hugging Face Transformers (--backend hf) or accelerate as compatibility fallbacks.
633. Start with a smoke test.
64 - inspect-ai: add --limit 10 or similar.
65 - lighteval: add --max-samples 10.
664. Scale up only after the smoke test passes.
675. If the user wants remote execution, hand off to hugging-face-jobs with the same script + args.
68
69# Quick Start
70
71## Option A: inspect-ai with local inference providers path
72
73Best when the model is already supported by Hugging Face Inference Providers and you want the lowest local setup overhead.
74
75```bash
76uv run scripts/inspect_eval_uv.py \
77 --model meta-llama/Llama-3.2-1B \
78 --task mmlu \
79 --limit 20
80```
81
82Use this path when:
83- you want a quick local smoke test
84- you do not need direct GPU control
85- the task already exists in inspect-evals
86
87## Option B: inspect-ai on Local GPU
88
89Best when you need to load the Hub model directly, use vllm, or fall back to Transformers for unsupported architectures.
90
91Local GPU:
92
93```bash
94uv run scripts/inspect_vllm_uv.py \
95 --model meta-llama/Llama-3.2-1B \
96 --task gsm8k \
97 --limit 20
98```
99
100Transformers fallback:
101
102```bash
103uv run scripts/inspect_vllm_uv.py \
104 --model microsoft/phi-2 \
105 --task mmlu \
106 --backend hf \
107 --trust-remote-code \
108 --limit 20
109```
110
111## Option C: lighteval on Local GPU
112
113Best when the task is naturally expressed as a lighteval task string, especially Open LLM Leaderboard style benchmarks.
114
115Local GPU:
116
117```bash
118uv run scripts/lighteval_vllm_uv.py \
119 --model meta-llama/Llama-3.2-3B-Instruct \
120 --tasks "leaderboard|mmlu|5,leaderboard|gsm8k|5" \
121 --max-samples 20 \
122 --use-chat-template
123```
124
125accelerate fallback:
126
127```bash
128uv run scripts/lighteval_vllm_uv.py \
129 --model microsoft/phi-2 \
130 --tasks "leaderboard|mmlu|5" \
131 --backend accelerate \
132 --trust-remote-code \
133 --max-samples 20
134```
135
136# Remote Execution Boundary
137
138This skill intentionally stops at **local execution and backend selection**.
139
140If the user wants to:
141- run these scripts on Hugging Face Jobs
142- pick remote hardware
143- pass secrets to remote jobs
144- schedule recurring runs
145- inspect / cancel / monitor jobs
146
147then switch to the **hugging-face-jobs** skill and pass it one of these scripts plus the chosen arguments.
148
149# Task Selection
150
151inspect-ai examples:
152- mmlu
153- gsm8k
154- hellaswag
155- arc_challenge
156- truthfulqa
157- winogrande
158- humaneval
159
160lighteval task strings use suite|task|num_fewshot:
161- leaderboard|mmlu|5
162- leaderboard|gsm8k|5
163- leaderboard|arc_challenge|25
164- lighteval|hellaswag|0
165
166Multiple lighteval tasks can be comma-separated in --tasks.
167
168# Backend Selection
169
170- Prefer inspect_vllm_uv.py --backend vllm for fast GPU inference on supported architectures.
171- Use inspect_vllm_uv.py --backend hf when vllm does not support the model.
172- Prefer lighteval_vllm_uv.py --backend vllm for throughput on supported models.
173- Use lighteval_vllm_uv.py --backend accelerate as the compatibility fallback.
174- Use inspect_eval_uv.py when Inference Providers already cover the model and you do not need direct GPU control.
175
176# Hardware Guidance
177
178| Model size | Suggested local hardware |
179|---|---|
180| < 3B | consumer GPU / Apple Silicon / small dev GPU |
181| 3B - 13B | stronger local GPU |
182| 13B+ | high-memory local GPU or hand off to hugging-face-jobs |
183
184For smoke tests, prefer cheaper local runs plus --limit or --max-samples.
185
186# Troubleshooting
187
188- CUDA or vLLM OOM:
189 - reduce --batch-size
190 - reduce --gpu-memory-utilization
191 - switch to a smaller model for the smoke test
192 - if necessary, hand off to hugging-face-jobs
193- Model unsupported by vllm:
194 - switch to --backend hf for inspect-ai
195 - switch to --backend accelerate for lighteval
196- Gated/private repo access fails:
197 - verify HF_TOKEN
198- Custom model code required:
199 - add --trust-remote-code
200
201# Examples
202
203See:
204- examples/USAGE_EXAMPLES.md for local command patterns
205- scripts/inspect_eval_uv.py
206- scripts/inspect_vllm_uv.py
207- scripts/lighteval_vllm_uv.py
208
In the file
SKILL.md929 words
Files6
LicenceApache-2.0
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈90
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
7,460
on trigger
The instruction body and 5 supporting files, read only when the skill fires.
3.8%
of a 200k window
Ten skills this size would take about 38% of the window before you open a file.
050k100k150k200k context window

7.5k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

Adoption
Installsnone yet
Ratingno reviews yet

The procedure it runs

The procedure has not been published here. It is in the skill’s own SKILL.md, which its author has not sent to the marketplace yet.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

It writes no files and reaches no network. All it changes is how the model reasons and writes.

What it asks for
Writes filesno
Network accessno

Read from the allowed-tools line of this skill’s own SKILL.md. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

6 files, 30.2 kB on disk. Mostly text — the instructions the model reads — with 3 scripts in it that your client would run only if the instructions tell it to.

  • SKILL.md6.6 kB
  • examples/.env.example0.2 kB
  • examples/USAGE_EXAMPLES.md2.1 kB
  • scripts/inspect_eval_uv.py3.0 kB
  • scripts/inspect_vllm_uv.py9.1 kB
  • scripts/lighteval_vllm_uv.py9.2 kB
What is not in it

A skill installs nothing and depends on nothing: it is a folder your client reads. This one carries 3 scripts beside the text, so the bundle is 6 files you can review in full before installing. The Apache-2.0 licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

$12 once
Hugging Face Community Evals · Apache-2.0 · huggingface
one-time
Price$12 once
LicenceApache-2.0 — the author’s, unchanged by this purchase
Paid throughStripe, once, on the card you add at the checkout
Keeps workingfor good — the files are yours once they are on disk
Updatesevery update its author ships, delivered through this account

You can read the whole bundle before paying — the SKILL.md above is the product, not a preview of it. What the money buys is the delivery: the folder packaged and handed to your machine by key, every update its author ships, and our support if it does not do what this listing says. The terms of use are Apache-2.0, set by the author and unchanged by buying it here.

Payment runs through Stripe, on a page like this one rather than a redirect. Once there is an account it joins the same mcprush invoice as everything else you run, so there is never a second card to enter.

Which clients pick it up on their own

A skill is a folder of text. A client with a skills folder reads it without being told; everywhere else the same text works, it is just handed to the model rather than found.

Claude Code.claude/skills/
Claude Desktop
ChatGPT
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok.grok/skills/
Zed.agents/skills/
Windsurf.windsurf/skills/
Agent SDK.claude/skills/
HTTP / API
This release
Versionnot versioned
Publishedno release date on file
Price$12
Referencehuggingface/hugging-face-community-evals

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

v
  • No earlier releases have been published to the marketplace.
Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

Reviews

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